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GO: a cluster algorithm for graph visualization


Huang, X and Huang, W, GO: a cluster algorithm for graph visualization, Journal of Visual Languages and Computing, 28 pp. 71-82. ISSN 1045-926X (2015) [Refereed Article]

Copyright Statement

Copyright 2014 Elsevier Ltd.

DOI: doi:10.1016/j.jvlc.2014.12.007


As we are in the big data age, graph data such as user networks in Facebook and Flickr becomes large. How to reduce the visual complexity of a graph layout is a challenging problem. Clustering graphs is regarded as one of effective ways to address this problem. Most of current graph visualization systems, however, directly use existing clustering algorithms that are not originally developed for the visualization purpose. For graph visualization, a clustering algorithm should meet specific requirements such as the sufficient size of clusters, and automatic determination of the number of clusters. After identifying the requirements of clustering graphs for visualization, in this paper we present a new clustering algorithm that is particularly designed for visualization so as to reduce the visual complexity of a layout, together with a strategy for improving the scalability of our algorithm. Experiments have demonstrated that our proposed algorithm is capable of detecting clusters in a way that is required in graph visualization.

Item Details

Item Type:Refereed Article
Keywords:information visualization, graph clustering, graph drawing
Research Division:Information and Computing Sciences
Research Group:Library and information studies
Research Field:Human information interaction and retrieval
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Huang, W (Dr Tony Huang)
ID Code:97809
Year Published:2015
Web of Science® Times Cited:5
Deposited By:Information and Communication Technology
Deposited On:2015-01-14
Last Modified:2018-03-28

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